code: def concat_dependencies(
      feature_name: str,
      dependencies: List[str],
      dependency_reducers: torch.ModuleDict,
      combiner_hidden_state: torch.Tensor,
      other_output_feature_states: Dict[str, torch.Tensor],
  ) -> torch.Tensor:
      """Concatenates combiner_hidden_state with other output feature hidden states based on listed dependencies."""
      # No dependencies.
      if not dependencies:
          return combiner_hidden_state
  
      dependency_hidden_states = []
      for feature_name in dependencies:
          # The dependent feature should be present since ECD does a topological sort over output features.
          feature_hidden_state = other_output_feature_states[
              feature_name
          ]
  
          # This feature is sequential.
          if len(combiner_hidden_state.shape) > 2:
              if len(feature_hidden_state.shape) > 2:
                  # The dependent feature is also sequential.
                  # matrix matrix -> concat
                  assert (
                      combiner_hidden_state.shape[1]
                      == feature_hidden_state.shape[1]
                  )
                  dependency_hidden_states.append(feature_hidden_state)
              else:
                  # The dependent feature is not sequential.
                  # matrix vector -> tile concat
                  sequence_max_length = combiner_hidden_state.shape[1]
                  multipliers = (1, sequence_max_length, 1)
                  tiled_representation = torch.tile(
                      torch.unsqueeze(feature_hidden_state, 1),
                      multipliers,
                  )
  
                  sequence_length = sequence_length_3D(
                      combiner_hidden_state
                  )
                  mask = sequence_mask(
                      sequence_length, sequence_max_length
                  )
                  tiled_representation = torch.mul(
                      tiled_representation,
                      mask[:, :, np.newaxis].type(torch.float32),
                  )
  
                  dependency_hidden_states.append(tiled_representation)
  
          else:
              # This feature is not sequential.
              if len(feature_hidden_state.shape) > 2:
                  # The dependent feature is sequential.
                  # vector matrix -> reduce concat
                  reducer = dependency_reducers[feature_name]
                  dependency_hidden_states.append(
                      reducer(feature_hidden_state)
                  )
              else:
                  # The dependent feature is not sequential.
                  # vector vector -> concat
                  dependency_hidden_states.append(feature_hidden_state)
  
      try:
          hidden = torch.cat(
              [combiner_hidden_state] + dependency_hidden_states, dim=-1
          )
      except Exception as e:
          raise ValueError(
              f"Shape mismatch {e} while concatenating dependent features of {feature_name}: "
              f"{dependencies}. Concatenating the feature activations tensor {combiner_hidden_state} "
              f"with activation tensors of dependencies: {dependency_hidden_states}. The error is "
              "likely due to a mismatch of the second dimension (sequence length) or a "
              "difference in ranks. Likely solutions are setting the maximum_sequence_length "
              "of all sequential features to be the same,  or reduce the output of some "
              "features, or disabling the bucketing setting bucketing_field to None / null, "
              "as activating it will reduce the length of the field the bucketing is "
              "performed on."
          )
      return hidden
  
file_path: ludwig/ludwig/utils/output_feature_utils.py
imports: from typing import Dict, List
  import numpy
  import torch
  from ludwig.utils.torch_utils import sequence_length_3D, sequence_mask
label_desc: Use image node_id 7 for calling a global function with example usage: concat_dependencies(feature_name, dependencies, dependency_reducers, combiner_hidden_state, other_output_feature_states) and returns: hidden, combiner_hidden_state